Recently, two prominent approaches for bootstrapping with big data have been proposed. Both of these methods are focused on finding computationally efficient implementations of the bootstrap approach rather than reducing its complexity. The first approach, known as the Poisson bootstrap, utilizes that bootstrap samples, i.e., multinomial sampling from the original sample, can be well approximated by Poisson frequencies [1]}. For example, [2]} showed that the Poisson bootstrap can be implemented in the MapReduce framework [3]}, which enables powerful parallelization on clusters of computers. [2]} implemented a non-parametric bootstrap in MapReduce for linear estimators [5]}, like means and sums or smooth functions thereof. Moreover, [2]} showed that semi-parametric estimators of non-linear estimators such as quantiles can be estimated using similar implementations. As an alternative to bootstrapping quantiles when samples are dependent, [7]} developed a method based on asymptotic arguments.
